Intelligent Regularized Measurement Procedure Based on the Use of a Dynamic Model of the Combined Maximum Principle and the Theory of Regularization

Abstract

Abstract A method for intellectualization of measurement procedures based on step-by-step optimization procedure is developed. The procedure reduces the stochastic estimation problem to a number of deterministic problems. The goal of the first stage of optimization is the synthesis of adaptive mathematical model of the measuring process based on the combined maximum principle. In contrast to the known methods this ensures the constructiveness of its use at the next stages of optimization. The goal of the subsequent stages of optimization is to solve the estimation problem based on the regularization method of A.N. Tikhonov. The equations for the iterative measurement procedure are obtained. The structure of their state transition vector functions differs from the known equations structure. This procedure belongs to the category of intelligent measurement procedures, since it makes a targeted choice of the parameter estimation closest to the true value the under the conditions of structural uncertainty of the model of the studied object and of parametric uncertainty of the observation model.